地铁站内、站间客流预测的物理-虚拟协同建模
Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
摘要：
由于地铁客流预测在现实生活中的广泛应用，地铁客流预测是智能交通系统中一项重要而又具有挑战性的任务。然而，传统的方法要么忽略了地铁系统的拓扑信息，要么直接学习的物理拓扑结构，并不能充分探索客流量的演变模式。为了解决这个问题，我们模拟了一个地铁系统的各种拓扑结构的图，并提出了一个统一的物理虚拟协作图网络（PVCGN），它可以有效地学习复杂的乘客量的模式，从量身定制的图。具体而言，基于地铁系统的实际拓扑结构直接构建物理图，在站间客流相似性和相关性的指导下，利用虚拟拓扑结构构建相似图和相关图。这些互补图被并入图卷积门控递归单元（GC-GRU）中用于时空表示学习。此外，一个全连接门控递归单元（FC-GRU）也被用来捕捉全局的演化趋势。最后，我们开发了一个Seq 2Seq模型与GC-GRU和FC-GRU预测未来的地铁客流量顺序。在两个大规模基准上进行了广泛的实验（例如，上海地铁和杭州地铁）的车站级地铁客流预测表明，我们的PVCGN的优越性。此外，我们将所提出的PVCGN应用于解决在线OD客流量预测，实验结果表明，我们的方法的通用性。我们的代码和基准可以在https://github.com/HCPLab-SYSU/PVCGN上找到。
英文摘要：
Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventional methods either ignore the topological information of metro systems or directly learn on physical topology, and cannot fully explore the patterns of ridership evolution. To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system, while a similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation. These complementary graphs are incorporated into a Graph Convolution Gated Recurrent Unit (GC-GRU) for spatial-temporal representation learning. Further, a Fully-Connected Gated Recurrent Unit (FC-GRU) is also applied to capture the global evolution tendency. Finally, we develop a Seq2Seq model with GC-GRU and FC-GRU to forecast the future metro ridership sequentially. Extensive experiments on two large-scale benchmarks (e.g., Shanghai Metro and Hangzhou Metro) well demonstrate the superiority of our PVCGN for station-level metro ridership prediction. Moreover, we apply the proposed PVCGN to address the online origin-destination (OD) ridership prediction and the experiment results show the universality of our method. Our code and benchmarks are available at https://github.com/HCPLab-SYSU/PVCGN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
